At PrometaAI, we lead business transformations through innovative AI solutions. Our end-to-end delivery approach ensures meaningful impact where it matters most. We are committed to providing reliable, responsible solutions that build trust and drive growth. Our platforms empower businesses to accelerate transformation across sectors like marketing, pricing, customer service, demand forecasting, and planning.

We’re seeking a Data Scientist to lead groundbreaking projects in recommender systems, personalization algorithms, and predictive modeling. Your expertise will drive the design of intelligent, scalable solutions that enhance user experiences and solve complex personalization challenges!

What You'll Do:

Recommender Systems: Develop and implement advanced recommender algorithms, including collaborative filtering, content-based filtering, and hybrid models, to enhance user personalization.
Personalization & Ranking: Optimize ranking algorithms and personalization strategies to improve user engagement and satisfaction.
Auxiliary Models: Develop machine learning and statistical models to support recommendation and personalization tasks, such as segmentation, churn prediction, text classification and named entity recognition.
Data Analysis & Feature Engineering: Analyze large datasets of user interactions and item attributes to extract meaningful features for recommender models.
Model Evaluation & Experimentation: Design and conduct A/B tests and offline evaluations to measure the performance of recommender systems and iterate on improvements.
Deep Learning for Recommendations: Apply deep learning techniques for retrieval and raking tasks.
Scalable Systems: Build and deploy scalable recommender system components such as data processing pipelines, micro-services, continious training pipelines.
Collaboration: Work closely with product managers, engineers, and other data scientists to translate business requirements into effective recommender solutions.
Research & Development: Stay up-to-date with the latest research in recommender systems and apply cutting-edge techniques to improve our products.

What We're Looking For:

Experience: 5+ years of experience in data science / ml engineering. Focus on developing and deploying recommender systems is a plus.
Algorithms & Techniques: Strong understanding of collaborative filtering, content-based filtering, matrix factorization, and deep learning for recommendations.
Tools & Frameworks: Proficiency in Python, scikit-learn, TensorFlow, PyTorch, Docker, FastAPI, SQL and Git
Big Data: Experience with data preprocessing, feature engineering using bigdata tools such as Apache Spark and Apache Beam.
Model Validation: Expertise in evaluating ML systems using back-testing, as well as conducting A/B tests on production environments.
Cloud Computing: Experience deploying ML solutions on cloud environments.
Software Development: Programming skills in Python with emphasis on writing modularized, clean and production level code.
Problem-Solving: Ability to analyze complex problems and develop innovative solutions.
Communication: Excellent communication skills and the ability to explain complex technical concepts to non-technical audiences.